What are Highrises?

In Toronto, as is the case in many urban cities, living in a highrise (or as my 6 year old says “living in a box in the sky”) is a common situation for many families. With larger populations, it is infeasible to distribute people horizontally while still maintaining proximity to the city centre, and therefore highrises provide housing in prime locations by distributing living spaces vertically. They allow the same proximities to various areas of the city core, with modest but adequate space, at a relatively inexpensive price range.

Our Focal Topic of Highrises: Fire Safety

Relative to traditional housing (think of the classic single family home with a driveway, small yard, and picket fence) there are a unique set of features when living in a highrise. Consider things like privacy as your neighbours are typically on the other side of a wall, ceiling, floor – or likely all three. There are also more important factors that imply risk to safety such as flooding or detrimentally more serious, fires.

Highrise Residential Fire Inspection – City of Toronto

Through the Open Data program of the City of Toronto, a dataset on fire inspection details is available, refreshed daily, containing data on inspections including any violations, open/close dates of inspections, and the address including ward. A snippet of the dataset:

## # A tibble: 3 x 9
##   `_id` Inspections.Closed… inspections.Opened… latitude longitude
##   <int> <dttm>              <dttm>                 <dbl>     <dbl>
## 1 22344 2017-06-27 11:09:47 2017-06-27 11:09:47     43.7     -79.5
## 2 22345 2017-02-06 12:27:28 2017-02-03 11:04:00     43.7     -79.5
## 3 22346 2017-01-16 10:30:24 2017-01-13 09:58:06     43.7     -79.6
## # … with 4 more variables: propertyAddress <fct>, propertyWard <fct>,
## #   violations.violationDescription <chr>,
## #   violations.violationFireCode <chr>

What that vis is?

For brevity, we’ll focus our analysis on the following two questions:

1. How do the number of inspections look over the time scale of the dataset? Are they steady, increasing, or decreasing?
2. How do inspections look by ward? Are there certain wards with many/few inspections? Are the inspections typically clear or are there violations?

There are some basic transformations to the data required for the visualizations, but we leave them contained in the RMarkdown document and not on this deck : )

Monthly Inspections over Time

Inspections by Ward, Clear v. Violations